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Weakening of the Target Signal Whitening at Inversion of the noise Correlation Matrix

Keywords:

B. N. Oreshkin, P. A. Bakulev


Detection of signals of moving targets in presence of passive noises is a complicated problem. Such problems as small effective scattering area of radar targets and a priori indeterminacy and nonstationary change of noise covariation matrix properties, more essential than those for passive noises sources, are solved by using adaptive detection systems. Adaptation of a system of radar signal processing is connected with inversion of covariation matrices for noises or their estimates. However, as a rule, such matrices are iss-conditioned. Moreover, contamination of a covariation matrix with a target signal yields essential lowering the noise suppression quality. Regularization of a covariation matrix estimate can be used to reduce its ill-conditionality and effect of its contamination. At the same time, the optimum value of a regularization coefficient cannot be found without introducing additional assumptions about the structure of a covariation matrix and the model of the useful signal penetration into a learning sequence. In the article, an iterative algorithm for optimization of the regularization coefficient is proposed. The algorithm is based on maximization of the Rayleigh empiric relation. The solution proposed does not employ any assumptions about the covariation matrix structure or the model of the useful signal penetration into a learning siquence. Results of the modeling demonstrate efficiency of the proposed algorithm for noise suppression
References:
May 29, 2020

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